Face recognition using nonparametric-weighted Fisherfaces

Dong Lin Li, Mukesh Prasad, Sheng-Chih Hsu, Chao Ting Hong, Chin-Teng Lin

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This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases.
Original languageEnglish
Article number92
JournalEurasip Journal on Advances in Signal Processing
StatePublished - 2012


  • appearance-based vision; face recognition; nonparametric-weighted feature extraction (NWFE)

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